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Title:  Comparison of discrimination methods for the classification of tumors using gene expression data

Authors: Sandrine Dudoit, Jane Fridlyand, and Terry Speed

Abstract:

A reliable and precise classification of tumors is essential for successful treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies which  are being used increasingly in cancer research. By allowing the monitoring of expression levels for thousands of genes simultaneously, such techniques may lead to a more complete understanding of the molecular  variations among tumors and hence to a finer and more informative  classification. The ability to successfully distinguish between tumor classes (already known or yet to be discovered) using gene expression data is an important aspect of this novel approach to cancer classification.

In this talk, we compare the performance of different discrimination methods for the classification of tumors based on gene expression profiles. These methods include: nearest-neighbor classifiers, linear discriminant analysis, and classification trees. In our comparison, we also consider recent machine learning approaches for aggregating predictors such as bagging and boosting.  The methods are applied to three recently published datasets: the leukemia (ALL/AML) dataset of Golub et al. (1999), the lymphoma dataset of Alizadeh et al. (2000), and the 60 cancer cell line (NCI 60) dataset of Ross et al. (2000).
 

Slides: Download [pdf files] [ps file] 

Full text:Technical report #576